Overview

Dataset statistics

Number of variables36
Number of observations10000
Missing cells237701
Missing cells (%)66.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory288.0 B

Variable types

Numeric11
Categorical10
Unsupported15

Alerts

UPN has a high cardinality: 8626 distinct valuesHigh cardinality
EntryDate has a high cardinality: 398 distinct valuesHigh cardinality
EnrolStatus is highly imbalanced (77.3%)Imbalance
Surname has 10000 (100.0%) missing valuesMissing
Forename has 10000 (100.0%) missing valuesMissing
Middlenames has 10000 (100.0%) missing valuesMissing
PreferredSurname has 10000 (100.0%) missing valuesMissing
FormerSurname has 10000 (100.0%) missing valuesMissing
DoB has 10000 (100.0%) missing valuesMissing
B has 10000 (100.0%) missing valuesMissing
J has 10000 (100.0%) missing valuesMissing
L has 4950 (49.5%) missing valuesMissing
P has 9117 (91.2%) missing valuesMissing
V has 6522 (65.2%) missing valuesMissing
W has 10000 (100.0%) missing valuesMissing
C has 8208 (82.1%) missing valuesMissing
E has 9996 (> 99.9%) missing valuesMissing
H has 10000 (100.0%) missing valuesMissing
I has 3718 (37.2%) missing valuesMissing
M has 6306 (63.1%) missing valuesMissing
R has 10000 (100.0%) missing valuesMissing
S has 10000 (100.0%) missing valuesMissing
T has 10000 (100.0%) missing valuesMissing
G has 9995 (> 99.9%) missing valuesMissing
N has 10000 (100.0%) missing valuesMissing
O has 7719 (77.2%) missing valuesMissing
U has 9890 (98.9%) missing valuesMissing
D has 10000 (100.0%) missing valuesMissing
X has 5672 (56.7%) missing valuesMissing
Y has 5608 (56.1%) missing valuesMissing
UPN is uniformly distributedUniform
Surname is an unsupported type, check if it needs cleaning or further analysisUnsupported
Forename is an unsupported type, check if it needs cleaning or further analysisUnsupported
Middlenames is an unsupported type, check if it needs cleaning or further analysisUnsupported
PreferredSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
FormerSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
DoB is an unsupported type, check if it needs cleaning or further analysisUnsupported
B is an unsupported type, check if it needs cleaning or further analysisUnsupported
J is an unsupported type, check if it needs cleaning or further analysisUnsupported
W is an unsupported type, check if it needs cleaning or further analysisUnsupported
H is an unsupported type, check if it needs cleaning or further analysisUnsupported
R is an unsupported type, check if it needs cleaning or further analysisUnsupported
S is an unsupported type, check if it needs cleaning or further analysisUnsupported
T is an unsupported type, check if it needs cleaning or further analysisUnsupported
N is an unsupported type, check if it needs cleaning or further analysisUnsupported
D is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-06-26 14:01:40.329716
Analysis finished2023-06-26 14:01:59.188251
Duration18.86 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Estab
Real number (ℝ)

Distinct9587
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189905.39
Minimum57985
Maximum293928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:01:59.268010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum57985
5-th percentile132361.35
Q1166171.75
median190595.5
Q3214003
95-th percentile247928.2
Maximum293928
Range235943
Interquartile range (IQR)47831.25

Descriptive statistics

Standard deviation35116.21
Coefficient of variation (CV)0.18491423
Kurtosis-0.10786674
Mean189905.39
Median Absolute Deviation (MAD)23888
Skewness-0.042528573
Sum1.8990539 × 109
Variance1.2331482 × 109
MonotonicityNot monotonic
2023-06-26T15:01:59.418345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
146550 3
 
< 0.1%
221353 3
 
< 0.1%
194420 3
 
< 0.1%
214034 3
 
< 0.1%
206150 3
 
< 0.1%
169253 3
 
< 0.1%
191300 3
 
< 0.1%
161702 3
 
< 0.1%
184952 3
 
< 0.1%
243467 3
 
< 0.1%
Other values (9577) 9970
99.7%
ValueCountFrequency (%)
57985 1
< 0.1%
65974 1
< 0.1%
71604 1
< 0.1%
74811 1
< 0.1%
74870 1
< 0.1%
74935 1
< 0.1%
77483 1
< 0.1%
77703 1
< 0.1%
77939 1
< 0.1%
81147 1
< 0.1%
ValueCountFrequency (%)
293928 1
< 0.1%
292083 1
< 0.1%
291610 1
< 0.1%
291551 1
< 0.1%
291246 1
< 0.1%
291008 1
< 0.1%
289771 1
< 0.1%
289670 1
< 0.1%
288886 1
< 0.1%
288203 1
< 0.1%

UPN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct8626
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
205cc514-0a71-4e71-a0d5-19fcc0f12e6f
 
5
7ae4dc3d-52f0-4db3-8f72-ad1a5334897c
 
4
49f5db9a-5e7c-49e8-a627-5bd8ad26482f
 
4
791ddd77-5f6f-4744-ab30-f26c48da4373
 
4
68f19184-fe32-46eb-bec5-898d0f9eea07
 
4
Other values (8621)
9979 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters360000
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7386 ?
Unique (%)73.9%

Sample

1st row2fa67447-1c17-4aa2-9aeb-6e79f83eb121
2nd row6bdd9ae9-8ce3-4fcf-88a1-773376ab8022
3rd row6d5dba99-f3f9-41aa-baa7-549132c6b1df
4th row7738a3e3-d441-4879-914d-2276196e946a
5th rowc5985061-04ff-41e1-a09f-31ee30ef9681

Common Values

ValueCountFrequency (%)
205cc514-0a71-4e71-a0d5-19fcc0f12e6f 5
 
0.1%
7ae4dc3d-52f0-4db3-8f72-ad1a5334897c 4
 
< 0.1%
49f5db9a-5e7c-49e8-a627-5bd8ad26482f 4
 
< 0.1%
791ddd77-5f6f-4744-ab30-f26c48da4373 4
 
< 0.1%
68f19184-fe32-46eb-bec5-898d0f9eea07 4
 
< 0.1%
4181bd3b-7cf3-4a90-ad92-994470d0a072 4
 
< 0.1%
0d2e2438-943b-44ce-a89a-1a63f7d60d05 4
 
< 0.1%
915f855f-b698-4d4f-880e-abc5d71dcebf 4
 
< 0.1%
abe4288e-c183-4e5f-bb25-214751f70a36 4
 
< 0.1%
a8efc11c-98d5-45fc-b4c1-083a4b698f2f 3
 
< 0.1%
Other values (8616) 9960
99.6%

Length

2023-06-26T15:01:59.544649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
205cc514-0a71-4e71-a0d5-19fcc0f12e6f 5
 
< 0.1%
49f5db9a-5e7c-49e8-a627-5bd8ad26482f 4
 
< 0.1%
791ddd77-5f6f-4744-ab30-f26c48da4373 4
 
< 0.1%
68f19184-fe32-46eb-bec5-898d0f9eea07 4
 
< 0.1%
4181bd3b-7cf3-4a90-ad92-994470d0a072 4
 
< 0.1%
0d2e2438-943b-44ce-a89a-1a63f7d60d05 4
 
< 0.1%
915f855f-b698-4d4f-880e-abc5d71dcebf 4
 
< 0.1%
abe4288e-c183-4e5f-bb25-214751f70a36 4
 
< 0.1%
7ae4dc3d-52f0-4db3-8f72-ad1a5334897c 4
 
< 0.1%
23601855-63d6-41d7-99e5-ad725fbc9af4 3
 
< 0.1%
Other values (8616) 9960
99.6%

Most occurring characters

ValueCountFrequency (%)
- 40000
 
11.1%
4 28670
 
8.0%
8 21340
 
5.9%
9 21297
 
5.9%
b 21203
 
5.9%
a 21111
 
5.9%
3 19005
 
5.3%
5 18952
 
5.3%
c 18916
 
5.3%
1 18906
 
5.3%
Other values (7) 130600
36.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 203047
56.4%
Lowercase Letter 116953
32.5%
Dash Punctuation 40000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 28670
14.1%
8 21340
10.5%
9 21297
10.5%
3 19005
9.4%
5 18952
9.3%
1 18906
9.3%
6 18797
9.3%
7 18782
9.3%
2 18688
9.2%
0 18610
9.2%
Lowercase Letter
ValueCountFrequency (%)
b 21203
18.1%
a 21111
18.1%
c 18916
16.2%
d 18642
15.9%
e 18636
15.9%
f 18445
15.8%
Dash Punctuation
ValueCountFrequency (%)
- 40000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 243047
67.5%
Latin 116953
32.5%

Most frequent character per script

Common
ValueCountFrequency (%)
- 40000
16.5%
4 28670
11.8%
8 21340
8.8%
9 21297
8.8%
3 19005
7.8%
5 18952
7.8%
1 18906
7.8%
6 18797
7.7%
7 18782
7.7%
2 18688
7.7%
Latin
ValueCountFrequency (%)
b 21203
18.1%
a 21111
18.1%
c 18916
16.2%
d 18642
15.9%
e 18636
15.9%
f 18445
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 40000
 
11.1%
4 28670
 
8.0%
8 21340
 
5.9%
9 21297
 
5.9%
b 21203
 
5.9%
a 21111
 
5.9%
3 19005
 
5.3%
5 18952
 
5.3%
c 18916
 
5.3%
1 18906
 
5.3%
Other values (7) 130600
36.3%

Surname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Forename
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Middlenames
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

PreferredSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

FormerSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
F
5098 
M
4902 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 5098
51.0%
M 4902
49.0%

Length

2023-06-26T15:01:59.646858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:01:59.762214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
f 5098
51.0%
m 4902
49.0%

Most occurring characters

ValueCountFrequency (%)
F 5098
51.0%
M 4902
49.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 5098
51.0%
M 4902
49.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 5098
51.0%
M 4902
49.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 5098
51.0%
M 4902
49.0%

DoB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

EnrolStatus
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
C
9081 
Leaver
 
905
M
 
9
S
 
5

Length

Max length6
Median length1
Mean length1.4525
Min length1

Characters and Unicode

Total characters14525
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 9081
90.8%
Leaver 905
 
9.0%
M 9
 
0.1%
S 5
 
0.1%

Length

2023-06-26T15:01:59.870466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:01:59.992591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
c 9081
90.8%
leaver 905
 
9.0%
m 9
 
0.1%
s 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
C 9081
62.5%
e 1810
 
12.5%
L 905
 
6.2%
a 905
 
6.2%
v 905
 
6.2%
r 905
 
6.2%
M 9
 
0.1%
S 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
68.8%
Lowercase Letter 4525
31.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 9081
90.8%
L 905
 
9.0%
M 9
 
0.1%
S 5
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 1810
40.0%
a 905
20.0%
v 905
20.0%
r 905
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14525
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 9081
62.5%
e 1810
 
12.5%
L 905
 
6.2%
a 905
 
6.2%
v 905
 
6.2%
r 905
 
6.2%
M 9
 
0.1%
S 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 9081
62.5%
e 1810
 
12.5%
L 905
 
6.2%
a 905
 
6.2%
v 905
 
6.2%
r 905
 
6.2%
M 9
 
0.1%
S 5
 
< 0.1%

EntryDate
Categorical

Distinct398
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2019-09-04 00:00:00
1149 
2021-09-03 00:00:00
1052 
2020-09-03 00:00:00
1044 
2017-09-06 00:00:00
862 
2018-09-07 00:00:00
617 
Other values (393)
5276 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters190000
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique163 ?
Unique (%)1.6%

Sample

1st row2019-09-04 00:00:00
2nd row2020-09-02 00:00:00
3rd row2018-09-07 00:00:00
4th row2017-09-06 00:00:00
5th row2021-09-03 00:00:00

Common Values

ValueCountFrequency (%)
2019-09-04 00:00:00 1149
 
11.5%
2021-09-03 00:00:00 1052
 
10.5%
2020-09-03 00:00:00 1044
 
10.4%
2017-09-06 00:00:00 862
 
8.6%
2018-09-07 00:00:00 617
 
6.2%
2021-09-02 00:00:00 405
 
4.0%
2018-09-06 00:00:00 401
 
4.0%
2018-09-05 00:00:00 388
 
3.9%
2020-09-02 00:00:00 382
 
3.8%
2020-09-01 00:00:00 366
 
3.7%
Other values (388) 3334
33.3%

Length

2023-06-26T15:02:00.095298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 10000
50.0%
2019-09-04 1149
 
5.7%
2021-09-03 1052
 
5.3%
2020-09-03 1044
 
5.2%
2017-09-06 862
 
4.3%
2018-09-07 617
 
3.1%
2021-09-02 405
 
2.0%
2018-09-06 401
 
2.0%
2018-09-05 388
 
1.9%
2020-09-02 382
 
1.9%
Other values (389) 3700
 
18.5%

Most occurring characters

ValueCountFrequency (%)
0 91341
48.1%
- 20000
 
10.5%
: 20000
 
10.5%
2 15895
 
8.4%
9 11384
 
6.0%
10000
 
5.3%
1 9901
 
5.2%
3 2716
 
1.4%
7 2254
 
1.2%
8 2192
 
1.2%
Other values (3) 4317
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140000
73.7%
Dash Punctuation 20000
 
10.5%
Other Punctuation 20000
 
10.5%
Space Separator 10000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91341
65.2%
2 15895
 
11.4%
9 11384
 
8.1%
1 9901
 
7.1%
3 2716
 
1.9%
7 2254
 
1.6%
8 2192
 
1.6%
4 1879
 
1.3%
6 1620
 
1.2%
5 818
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 20000
100.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91341
48.1%
- 20000
 
10.5%
: 20000
 
10.5%
2 15895
 
8.4%
9 11384
 
6.0%
10000
 
5.3%
1 9901
 
5.2%
3 2716
 
1.4%
7 2254
 
1.2%
8 2192
 
1.2%
Other values (3) 4317
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91341
48.1%
- 20000
 
10.5%
: 20000
 
10.5%
2 15895
 
8.4%
9 11384
 
6.0%
10000
 
5.3%
1 9901
 
5.2%
3 2716
 
1.4%
7 2254
 
1.2%
8 2192
 
1.2%
Other values (3) 4317
 
2.3%

NCyearActual
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
10
2067 
9
2015 
8
2005 
7
1897 
11
1654 

Length

Max length6
Median length1
Mean length1.5531
Min length1

Characters and Unicode

Total characters15531
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row7
3rd row9
4th row7
5th row11

Common Values

ValueCountFrequency (%)
10 2067
20.7%
9 2015
20.2%
8 2005
20.1%
7 1897
19.0%
11 1654
16.5%
Leaver 362
 
3.6%

Length

2023-06-26T15:02:00.213411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:02:00.336103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
10 2067
20.7%
9 2015
20.2%
8 2005
20.1%
7 1897
19.0%
11 1654
16.5%
leaver 362
 
3.6%

Most occurring characters

ValueCountFrequency (%)
1 5375
34.6%
0 2067
 
13.3%
9 2015
 
13.0%
8 2005
 
12.9%
7 1897
 
12.2%
e 724
 
4.7%
L 362
 
2.3%
a 362
 
2.3%
v 362
 
2.3%
r 362
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13359
86.0%
Lowercase Letter 1810
 
11.7%
Uppercase Letter 362
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5375
40.2%
0 2067
 
15.5%
9 2015
 
15.1%
8 2005
 
15.0%
7 1897
 
14.2%
Lowercase Letter
ValueCountFrequency (%)
e 724
40.0%
a 362
20.0%
v 362
20.0%
r 362
20.0%
Uppercase Letter
ValueCountFrequency (%)
L 362
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13359
86.0%
Latin 2172
 
14.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5375
40.2%
0 2067
 
15.5%
9 2015
 
15.1%
8 2005
 
15.0%
7 1897
 
14.2%
Latin
ValueCountFrequency (%)
e 724
33.3%
L 362
16.7%
a 362
16.7%
v 362
16.7%
r 362
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15531
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5375
34.6%
0 2067
 
13.3%
9 2015
 
13.0%
8 2005
 
12.9%
7 1897
 
12.2%
e 724
 
4.7%
L 362
 
2.3%
a 362
 
2.3%
v 362
 
2.3%
r 362
 
2.3%

TermlySessionsPossible
Real number (ℝ)

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.741
Minimum66
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:02:00.633482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile104
Q1121
median130
Q3138
95-th percentile143
Maximum144
Range78
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.218537
Coefficient of variation (CV)0.095650864
Kurtosis0.63392224
Mean127.741
Median Absolute Deviation (MAD)8
Skewness-0.95112932
Sum1277410
Variance149.29265
MonotonicityNot monotonic
2023-06-26T15:02:00.787731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138 405
 
4.0%
140 397
 
4.0%
139 392
 
3.9%
143 383
 
3.8%
141 379
 
3.8%
135 369
 
3.7%
137 365
 
3.6%
131 361
 
3.6%
142 356
 
3.6%
136 354
 
3.5%
Other values (61) 6239
62.4%
ValueCountFrequency (%)
66 1
 
< 0.1%
75 1
 
< 0.1%
76 2
 
< 0.1%
77 3
< 0.1%
78 3
< 0.1%
79 2
 
< 0.1%
80 1
 
< 0.1%
81 4
< 0.1%
82 6
0.1%
83 4
< 0.1%
ValueCountFrequency (%)
144 198
2.0%
143 383
3.8%
142 356
3.6%
141 379
3.8%
140 397
4.0%
139 392
3.9%
138 405
4.0%
137 365
3.6%
136 354
3.5%
135 369
3.7%

/
Real number (ℝ)

Distinct68
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.2654
Minimum16
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:02:00.973175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile40
Q151
median58
Q366
95-th percentile76
Maximum87
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.677845
Coefficient of variation (CV)0.18326219
Kurtosis-0.15044259
Mean58.2654
Median Absolute Deviation (MAD)7
Skewness-0.060005437
Sum582654
Variance114.01636
MonotonicityNot monotonic
2023-06-26T15:02:01.133585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 383
 
3.8%
59 381
 
3.8%
55 372
 
3.7%
54 358
 
3.6%
61 356
 
3.6%
56 353
 
3.5%
53 350
 
3.5%
62 350
 
3.5%
63 345
 
3.5%
57 345
 
3.5%
Other values (58) 6407
64.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
19 1
 
< 0.1%
22 1
 
< 0.1%
23 2
 
< 0.1%
24 4
 
< 0.1%
25 2
 
< 0.1%
26 6
0.1%
27 3
 
< 0.1%
28 6
0.1%
29 10
0.1%
ValueCountFrequency (%)
87 7
 
0.1%
86 20
 
0.2%
85 19
 
0.2%
84 22
 
0.2%
83 37
0.4%
82 27
0.3%
81 42
0.4%
80 51
0.5%
79 54
0.5%
78 64
0.6%

\
Real number (ℝ)

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.24
Minimum13
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:02:01.277603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile41
Q154
median61
Q367
95-th percentile71
Maximum72
Range59
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.3257867
Coefficient of variation (CV)0.15742381
Kurtosis0.80296974
Mean59.24
Median Absolute Deviation (MAD)6
Skewness-0.94694434
Sum592400
Variance86.970297
MonotonicityNot monotonic
2023-06-26T15:02:01.413883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 508
 
5.1%
69 486
 
4.9%
70 483
 
4.8%
66 470
 
4.7%
62 464
 
4.6%
71 450
 
4.5%
68 436
 
4.4%
64 424
 
4.2%
65 420
 
4.2%
63 415
 
4.2%
Other values (47) 5444
54.4%
ValueCountFrequency (%)
13 1
 
< 0.1%
15 2
 
< 0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
20 3
< 0.1%
21 5
0.1%
22 4
< 0.1%
23 5
0.1%
24 5
0.1%
25 6
0.1%
ValueCountFrequency (%)
72 226
2.3%
71 450
4.5%
70 483
4.8%
69 486
4.9%
68 436
4.4%
67 508
5.1%
66 470
4.7%
65 420
4.2%
64 424
4.2%
63 415
4.2%

B
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

J
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

L
Real number (ℝ)

Distinct24
Distinct (%)0.5%
Missing4950
Missing (%)49.5%
Infinite0
Infinite (%)0.0%
Mean5.0833663
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:02:01.547316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile12
Maximum30
Range29
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4227433
Coefficient of variation (CV)0.67332218
Kurtosis1.8172331
Mean5.0833663
Median Absolute Deviation (MAD)2
Skewness1.2095814
Sum25671
Variance11.715172
MonotonicityNot monotonic
2023-06-26T15:02:01.666046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 828
 
8.3%
3 729
 
7.3%
4 611
 
6.1%
5 529
 
5.3%
1 491
 
4.9%
6 447
 
4.5%
7 339
 
3.4%
8 285
 
2.9%
9 223
 
2.2%
10 162
 
1.6%
Other values (14) 406
 
4.1%
(Missing) 4950
49.5%
ValueCountFrequency (%)
1 491
4.9%
2 828
8.3%
3 729
7.3%
4 611
6.1%
5 529
5.3%
6 447
4.5%
7 339
3.4%
8 285
 
2.9%
9 223
 
2.2%
10 162
 
1.6%
ValueCountFrequency (%)
30 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
20 5
 
0.1%
19 1
 
< 0.1%
18 12
 
0.1%
17 8
 
0.1%
16 25
0.2%
15 35
0.4%

P
Categorical

Distinct3
Distinct (%)0.3%
Missing9117
Missing (%)91.2%
Memory size78.2 KiB
1.0
631 
2.0
224 
3.0
 
28

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2649
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 631
 
6.3%
2.0 224
 
2.2%
3.0 28
 
0.3%
(Missing) 9117
91.2%

Length

2023-06-26T15:02:01.801321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:02:01.940460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 631
71.5%
2.0 224
 
25.4%
3.0 28
 
3.2%

Most occurring characters

ValueCountFrequency (%)
. 883
33.3%
0 883
33.3%
1 631
23.8%
2 224
 
8.5%
3 28
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1766
66.7%
Other Punctuation 883
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 883
50.0%
1 631
35.7%
2 224
 
12.7%
3 28
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 883
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2649
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 883
33.3%
0 883
33.3%
1 631
23.8%
2 224
 
8.5%
3 28
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 883
33.3%
0 883
33.3%
1 631
23.8%
2 224
 
8.5%
3 28
 
1.1%

V
Categorical

Distinct4
Distinct (%)0.1%
Missing6522
Missing (%)65.2%
Memory size78.2 KiB
2.0
1694 
1.0
1517 
3.0
258 
4.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10434
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 1694
 
16.9%
1.0 1517
 
15.2%
3.0 258
 
2.6%
4.0 9
 
0.1%
(Missing) 6522
65.2%

Length

2023-06-26T15:02:02.049819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:02:02.171309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1694
48.7%
1.0 1517
43.6%
3.0 258
 
7.4%
4.0 9
 
0.3%

Most occurring characters

ValueCountFrequency (%)
. 3478
33.3%
0 3478
33.3%
2 1694
16.2%
1 1517
14.5%
3 258
 
2.5%
4 9
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6956
66.7%
Other Punctuation 3478
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3478
50.0%
2 1694
24.4%
1 1517
21.8%
3 258
 
3.7%
4 9
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 3478
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10434
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3478
33.3%
0 3478
33.3%
2 1694
16.2%
1 1517
14.5%
3 258
 
2.5%
4 9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3478
33.3%
0 3478
33.3%
2 1694
16.2%
1 1517
14.5%
3 258
 
2.5%
4 9
 
0.1%

W
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

C
Real number (ℝ)

Distinct18
Distinct (%)1.0%
Missing8208
Missing (%)82.1%
Infinite0
Infinite (%)0.0%
Mean4.1729911
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:02:02.262353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile10
Maximum19
Range18
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.942438
Coefficient of variation (CV)0.70511485
Kurtosis2.3678834
Mean4.1729911
Median Absolute Deviation (MAD)2
Skewness1.425616
Sum7478
Variance8.6579412
MonotonicityNot monotonic
2023-06-26T15:02:02.360398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 378
 
3.8%
3 279
 
2.8%
1 262
 
2.6%
4 234
 
2.3%
5 192
 
1.9%
6 115
 
1.1%
7 104
 
1.0%
8 73
 
0.7%
9 45
 
0.4%
10 36
 
0.4%
Other values (8) 74
 
0.7%
(Missing) 8208
82.1%
ValueCountFrequency (%)
1 262
2.6%
2 378
3.8%
3 279
2.8%
4 234
2.3%
5 192
1.9%
6 115
 
1.1%
7 104
 
1.0%
8 73
 
0.7%
9 45
 
0.4%
10 36
 
0.4%
ValueCountFrequency (%)
19 1
 
< 0.1%
17 5
 
0.1%
16 4
 
< 0.1%
15 3
 
< 0.1%
14 13
 
0.1%
13 9
 
0.1%
12 19
0.2%
11 20
0.2%
10 36
0.4%
9 45
0.4%

E
Categorical

Distinct2
Distinct (%)50.0%
Missing9996
Missing (%)> 99.9%
Memory size78.2 KiB
5.0
3.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)25.0%

Sample

1st row3.0
2nd row5.0
3rd row5.0
4th row5.0

Common Values

ValueCountFrequency (%)
5.0 3
 
< 0.1%
3.0 1
 
< 0.1%
(Missing) 9996
> 99.9%

Length

2023-06-26T15:02:02.478257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:02:02.589325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0 3
75.0%
3.0 1
 
25.0%

Most occurring characters

ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
5 3
25.0%
3 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8
66.7%
Other Punctuation 4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4
50.0%
5 3
37.5%
3 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
5 3
25.0%
3 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4
33.3%
0 4
33.3%
5 3
25.0%
3 1
 
8.3%

H
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

I
Real number (ℝ)

Distinct45
Distinct (%)0.7%
Missing3718
Missing (%)37.2%
Infinite0
Infinite (%)0.0%
Mean10.412289
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:02:02.697069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median9
Q315
95-th percentile24
Maximum52
Range51
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.2735152
Coefficient of variation (CV)0.69855102
Kurtosis0.91189774
Mean10.412289
Median Absolute Deviation (MAD)5
Skewness1.0066779
Sum65410
Variance52.904023
MonotonicityNot monotonic
2023-06-26T15:02:02.843027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
3 472
 
4.7%
2 422
 
4.2%
4 420
 
4.2%
5 399
 
4.0%
10 361
 
3.6%
6 356
 
3.6%
9 346
 
3.5%
7 340
 
3.4%
8 307
 
3.1%
11 291
 
2.9%
Other values (35) 2568
25.7%
(Missing) 3718
37.2%
ValueCountFrequency (%)
1 239
2.4%
2 422
4.2%
3 472
4.7%
4 420
4.2%
5 399
4.0%
6 356
3.6%
7 340
3.4%
8 307
3.1%
9 346
3.5%
10 361
3.6%
ValueCountFrequency (%)
52 1
 
< 0.1%
46 1
 
< 0.1%
43 1
 
< 0.1%
42 1
 
< 0.1%
41 2
 
< 0.1%
40 4
< 0.1%
39 2
 
< 0.1%
38 1
 
< 0.1%
37 7
0.1%
36 3
< 0.1%

M
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing6306
Missing (%)63.1%
Infinite0
Infinite (%)0.0%
Mean1.8941527
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:02:02.988280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8924896
Coefficient of variation (CV)0.47118145
Kurtosis0.77289862
Mean1.8941527
Median Absolute Deviation (MAD)1
Skewness0.94951413
Sum6997
Variance0.79653769
MonotonicityNot monotonic
2023-06-26T15:02:03.122444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 1509
 
15.1%
1 1407
 
14.1%
3 580
 
5.8%
4 160
 
1.6%
5 36
 
0.4%
6 2
 
< 0.1%
(Missing) 6306
63.1%
ValueCountFrequency (%)
1 1407
14.1%
2 1509
15.1%
3 580
 
5.8%
4 160
 
1.6%
5 36
 
0.4%
6 2
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 36
 
0.4%
4 160
 
1.6%
3 580
 
5.8%
2 1509
15.1%
1 1407
14.1%

R
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

S
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

T
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

G
Categorical

Distinct3
Distinct (%)60.0%
Missing9995
Missing (%)> 99.9%
Memory size78.2 KiB
7.0
6.0
4.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)40.0%

Sample

1st row6.0
2nd row7.0
3rd row7.0
4th row4.0
5th row7.0

Common Values

ValueCountFrequency (%)
7.0 3
 
< 0.1%
6.0 1
 
< 0.1%
4.0 1
 
< 0.1%
(Missing) 9995
> 99.9%

Length

2023-06-26T15:02:03.240539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:02:03.355733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
7.0 3
60.0%
6.0 1
 
20.0%
4.0 1
 
20.0%

Most occurring characters

ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
7 3
20.0%
6 1
 
6.7%
4 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5
50.0%
7 3
30.0%
6 1
 
10.0%
4 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
7 3
20.0%
6 1
 
6.7%
4 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
7 3
20.0%
6 1
 
6.7%
4 1
 
6.7%

N
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

O
Real number (ℝ)

Distinct29
Distinct (%)1.3%
Missing7719
Missing (%)77.2%
Infinite0
Infinite (%)0.0%
Mean6.3160894
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:02:03.463458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q39
95-th percentile16
Maximum30
Range29
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.7847116
Coefficient of variation (CV)0.75754335
Kurtosis2.0423369
Mean6.3160894
Median Absolute Deviation (MAD)3
Skewness1.3373435
Sum14407
Variance22.893465
MonotonicityNot monotonic
2023-06-26T15:02:03.581303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 341
 
3.4%
3 268
 
2.7%
4 230
 
2.3%
1 202
 
2.0%
5 197
 
2.0%
6 172
 
1.7%
7 139
 
1.4%
8 129
 
1.3%
9 116
 
1.2%
10 80
 
0.8%
Other values (19) 407
 
4.1%
(Missing) 7719
77.2%
ValueCountFrequency (%)
1 202
2.0%
2 341
3.4%
3 268
2.7%
4 230
2.3%
5 197
2.0%
6 172
1.7%
7 139
1.4%
8 129
 
1.3%
9 116
 
1.2%
10 80
 
0.8%
ValueCountFrequency (%)
30 2
 
< 0.1%
29 4
< 0.1%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 3
 
< 0.1%
24 3
 
< 0.1%
23 3
 
< 0.1%
22 5
0.1%
21 7
0.1%
20 8
0.1%

U
Categorical

Distinct2
Distinct (%)1.8%
Missing9890
Missing (%)98.9%
Memory size78.2 KiB
1.0
75 
2.0
35 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters330
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 75
 
0.8%
2.0 35
 
0.4%
(Missing) 9890
98.9%

Length

2023-06-26T15:02:03.706780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:02:03.822939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 75
68.2%
2.0 35
31.8%

Most occurring characters

ValueCountFrequency (%)
. 110
33.3%
0 110
33.3%
1 75
22.7%
2 35
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 220
66.7%
Other Punctuation 110
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 110
50.0%
1 75
34.1%
2 35
 
15.9%
Other Punctuation
ValueCountFrequency (%)
. 110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 110
33.3%
0 110
33.3%
1 75
22.7%
2 35
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 110
33.3%
0 110
33.3%
1 75
22.7%
2 35
 
10.6%

D
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

X
Real number (ℝ)

Distinct18
Distinct (%)0.4%
Missing5672
Missing (%)56.7%
Infinite0
Infinite (%)0.0%
Mean6.1980129
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:02:03.925244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q38
95-th percentile12
Maximum18
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1864234
Coefficient of variation (CV)0.51410402
Kurtosis-0.10398097
Mean6.1980129
Median Absolute Deviation (MAD)2
Skewness0.53971118
Sum26825
Variance10.153294
MonotonicityNot monotonic
2023-06-26T15:02:04.042300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
5 512
 
5.1%
6 509
 
5.1%
4 481
 
4.8%
7 465
 
4.7%
3 443
 
4.4%
8 383
 
3.8%
2 366
 
3.7%
9 313
 
3.1%
10 245
 
2.5%
11 175
 
1.8%
Other values (8) 436
 
4.4%
(Missing) 5672
56.7%
ValueCountFrequency (%)
1 168
 
1.7%
2 366
3.7%
3 443
4.4%
4 481
4.8%
5 512
5.1%
6 509
5.1%
7 465
4.7%
8 383
3.8%
9 313
3.1%
10 245
2.5%
ValueCountFrequency (%)
18 3
 
< 0.1%
17 7
 
0.1%
16 6
 
0.1%
15 28
 
0.3%
14 47
 
0.5%
13 76
 
0.8%
12 101
 
1.0%
11 175
1.8%
10 245
2.5%
9 313
3.1%

Y
Real number (ℝ)

Distinct11
Distinct (%)0.3%
Missing5608
Missing (%)56.1%
Infinite0
Infinite (%)0.0%
Mean4.2415756
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:02:04.163840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile7
Maximum11
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8107128
Coefficient of variation (CV)0.42689628
Kurtosis-0.18956594
Mean4.2415756
Median Absolute Deviation (MAD)1
Skewness0.39458317
Sum18629
Variance3.278681
MonotonicityNot monotonic
2023-06-26T15:02:04.260312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4 922
 
9.2%
3 834
 
8.3%
5 764
 
7.6%
2 607
 
6.1%
6 581
 
5.8%
7 298
 
3.0%
1 191
 
1.9%
8 131
 
1.3%
9 50
 
0.5%
10 10
 
0.1%
(Missing) 5608
56.1%
ValueCountFrequency (%)
1 191
 
1.9%
2 607
6.1%
3 834
8.3%
4 922
9.2%
5 764
7.6%
6 581
5.8%
7 298
 
3.0%
8 131
 
1.3%
9 50
 
0.5%
10 10
 
0.1%
ValueCountFrequency (%)
11 4
 
< 0.1%
10 10
 
0.1%
9 50
 
0.5%
8 131
 
1.3%
7 298
 
3.0%
6 581
5.8%
5 764
7.6%
4 922
9.2%
3 834
8.3%
2 607
6.1%

Interactions

2023-06-26T15:01:56.709476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:41.416825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:43.010112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:44.503622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:46.007828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:47.551528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:49.250146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:50.756650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:52.157383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:53.626330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:55.108512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:56.835419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:41.608053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:43.134698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:44.633457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:46.136357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:47.852984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:49.380129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:50.886113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:52.272211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:53.780220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:55.395350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:56.973010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:41.786265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:43.285168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:44.784652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:46.270210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:48.026318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:49.527520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:51.015522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:52.398361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:53.911406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:55.506395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:57.095823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:41.949482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:43.417839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:44.943411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:46.407268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:48.162554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:49.671209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:51.149618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:52.553677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:54.037927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:55.659682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:57.212093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:42.083541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:43.567320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:45.075612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:46.534366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:48.311106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:49.810191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:51.275138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:52.695093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:54.156432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:55.827785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:57.319441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:42.219458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:43.700562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:45.212345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:46.686911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:48.473440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:49.969541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:51.395346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:52.825381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:54.276470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:55.965319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:57.440341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:42.348688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:43.837283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:45.347846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:46.837615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:48.624147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:50.102561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:51.526739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:52.948596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:54.389893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:56.098722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:57.552751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:42.475219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:43.973080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:45.473550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:46.963449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:48.767220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:50.210280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:51.647965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:53.057492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:54.514368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:56.216988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:57.677293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:42.601340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:44.102739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:45.599586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:47.105165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:48.887213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:50.328122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:51.770223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:53.175242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:54.680426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:56.333604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:57.814926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:42.746105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:44.237306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:45.733761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:47.256600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:49.015751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:50.467153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:51.892318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:53.324558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:54.844240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:56.453738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:57.962160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:42.878084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:44.376521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:45.877221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:47.402915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:49.144520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:50.625387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:52.037454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:53.463589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:54.985222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:01:56.584840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2023-06-26T15:01:58.185323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-26T15:01:58.691732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-26T15:01:59.060547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossible/\BJLPVWCEHIMRSTGNOUDXY
01209602fa67447-1c17-4aa2-9aeb-6e79f83eb121NaNNaNNaNNaNNaNFNaNC2019-09-04 00:00:0081095145NaNNaN4.01.02.0NaN2.0NaNNaN9.0NaNNaNNaNNaNNaNNaN4.0NaNNaN5.03.0
11753066bdd9ae9-8ce3-4fcf-88a1-773376ab8022NaNNaNNaNNaNNaNFNaNC2020-09-02 00:00:0071297469NaNNaN4.0NaNNaNNaNNaNNaNNaN2.0NaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaN
21496706d5dba99-f3f9-41aa-baa7-549132c6b1dfNaNNaNNaNNaNNaNFNaNC2018-09-07 00:00:0091256669NaNNaNNaNNaN3.0NaNNaNNaNNaN6.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
31879197738a3e3-d441-4879-914d-2276196e946aNaNNaNNaNNaNNaNFNaNC2017-09-06 00:00:0071314557NaNNaN6.0NaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaN6.0NaNNaNNaNNaN
4157625c5985061-04ff-41e1-a09f-31ee30ef9681NaNNaNNaNNaNNaNFNaNC2021-09-03 00:00:00111135253NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3.0NaNNaNNaNNaNNaN3.0NaNNaN4.05.0
5243467415e5a2f-b899-48bb-b8e6-fad0952d5fcfNaNNaNNaNNaNNaNFNaNC2019-05-03 00:00:0091164651NaNNaN2.0NaN2.0NaN2.0NaNNaN16.0NaNNaNNaNNaNNaNNaNNaNNaNNaN11.0NaN
61704262a82f4d5-e7b4-4bcf-8875-18b275c0e0e0NaNNaNNaNNaNNaNFNaNC2020-09-02 00:00:0071385662NaNNaNNaNNaN2.0NaNNaNNaNNaN21.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1.04.0
7205482d6ae4516-9346-4187-9d92-b4d64ff564f2NaNNaNNaNNaNNaNFNaNLeaver2018-09-04 00:00:0081194753NaNNaN8.0NaNNaNNaNNaNNaNNaN7.0NaNNaNNaNNaNNaNNaN15.01.0NaN10.0NaN
8218531191547cc-826d-4759-8516-7e5f16b0a07aNaNNaNNaNNaNNaNFNaNC2021-09-02 00:00:0091195766NaNNaN4.0NaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9183865e00b21b3-07e4-43cf-8a81-9c87daec498dNaNNaNNaNNaNNaNFNaNC2018-09-07 00:00:0091145045NaNNaNNaNNaNNaNNaN2.0NaNNaN19.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.0
EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossible/\BJLPVWCEHIMRSTGNOUDXY
9990201849ea65961c-5da4-45da-b8fb-5d42326bfa2cNaNNaNNaNNaNNaNFNaNC2021-09-03 00:00:0071445058NaNNaNNaNNaN1.0NaNNaNNaNNaN25.0NaNNaNNaNNaNNaNNaN4.0NaNNaNNaNNaN
9991171469fbde9aee-ba78-44d3-be56-f2b7aea3eab9NaNNaNNaNNaNNaNMNaNLeaver2018-09-07 00:00:0010974251NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.0NaN
99921513267937db75-a6b7-4fdf-ab9c-45bd63133598NaNNaNNaNNaNNaNFNaNC2021-09-03 00:00:0091054959NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3.0
9993222001d08bd991-5890-415c-bc4b-c9a97c3e5254NaNNaNNaNNaNNaNFNaNC2021-09-01 00:00:0071023844NaNNaNNaNNaN3.0NaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99941380098ac9f620-0a10-47e2-8eda-bd8e80a1065cNaNNaNNaNNaNNaNFNaNC2018-09-03 00:00:0081014738NaNNaNNaNNaN1.0NaNNaNNaNNaN13.0NaNNaNNaNNaNNaNNaN7.0NaNNaN4.0NaN
9995189635bb5b001f-4e11-462b-9bf7-ffc56987c04dNaNNaNNaNNaNNaNFNaNC2019-09-02 00:00:0091407372NaNNaN2.0NaN3.0NaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaN5.0
9996222094cee4d881-5636-4a05-8d2d-d022d64a1950NaNNaNNaNNaNNaNMNaNC2020-09-23 00:00:00101406566NaNNaN2.0NaNNaNNaNNaNNaNNaN10.03.0NaNNaNNaNNaNNaNNaNNaNNaNNaN5.0
9997223559f577e065-5c5d-4885-86ef-0b9a1d56fe61NaNNaNNaNNaNNaNMNaNC2019-09-02 00:00:00111375055NaNNaNNaNNaNNaNNaNNaNNaNNaN24.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0
99982404860406d49d-49a3-4ed0-8ee6-ad45bfd00d04NaNNaNNaNNaNNaNMNaNC2021-01-04 00:00:0071407471NaNNaNNaNNaNNaNNaNNaNNaNNaN3.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9999210714f0659776-d80e-481e-baa3-cf59b8e343a9NaNNaNNaNNaNNaNFNaNC2018-09-07 00:00:0011963341NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaN8.04.0